Object Centric Learning Framework (OCLF)

What is OCLF?

OCLF (Object Centric Learning framework) is a framework designed to ease running experiments for object centric learning research, yet is not limited to this use case. At its heart lies the idea that while code is not typically composable many experiments in machine learning very similar with minor changes and only represent minor changes.

One such example is multi-task training where a model might be trained to solve multiple tasks at the same time. Different ablations of said model would then contain different model components but largely remain the same.

OCLF allows for such ablations without creating duplicate code by defining models and experiments in configuration files and allowing their composition in configuration space via hydra.

Quickstart - Development setup

Installing OCLF requires at least python3.8. Installation can be done using poetry. After installing poetry, check out the repo and setup a development environment:

bash git clone https://github.com/amazon-science/object-centric-learning-framework.git cd object-centric-learning-framework poetry install

This installs the ocl package and the cli scripts used for running experiments in a poetry managed virtual environment.

Next we need to prepare a dataset. For this follow the steps below to install the dependencies needed for dataset conversion and creation.

bash cd scripts/datasets poetry install bash download_and_convert.sh movi_c

This should create a webdataset in the path scripts/datasets/outputs/movi_c.

After exposing this dataset to OCLF, a first experiment can be run:

bash cd ../.. # Go back to root folder export DATASET_PREFIX=scripts/datasets/outputs # Expose dataset path poetry run ocl_train +experiment=slot_attention/movi_c # Run training exeriment

The output of the training run should be stored at outputs/slot_attention/movi_c/<timestamp>.

For a more detailed guide on how to install, setup, and use OCLF check out the Tutorial in the docs.

Citation

If you use OCLF to run experiments in your work please cite it using the bibtex entry below

bibtex @misc{oclf, author = {Max Horn and Maximilian Seitzer and Andrii Zadaianchuk and Zixu Zhao and Dominik Zietlow and Florian Wenzel and Tianjun Xiao}, title = {Object Centric Learning Framework (version 0.1)}, year = {2023}, url = {https://github.com/amazon-science/object-centric-learning-framework}, }

Publications

Experiments for the following publications where run using OCLF. Please feel free to add your own experiments via pull requests and to list them below.

• M.Seitzer et al., Bridging the Gap to Real-World Object-Centric Learning training configurations evaluation configurations

License

This project is licensed under the Apache-2.0 License.

Contributing

We are happy to accept code contributions in the form of pull-requests and kindly ask contributors to follow the guidance provided below and in CONTRIBUTING.md.

We are using pre-commit to manage automatic code formatting and linting. For someone who has never worked with pre-commit, this can be a bit unusual. pre-commit works by setting up a Git commit hook that runs before each git commit. The hook executes a set of tests and automatic formatting on all files that are modified by the commit: - If a file does not pass a test, the commit is aborted and you are required to fix the problems, git add the files and run git commit again. - If a file is automatically formatted, the commit is also aborted. You can review the proposed changes using git diff, accept them with git add and run git commit again.

It can also make sense to manually run the hooks on all files in the repository (using pre-commit run -a) before committing, to make sure the commit passes. Note that this does not run the hooks on files which are not yet commited to the repository.

Important: make sure to run pre-commit within the environment installed by poetry. Otherwise the checks might fail because the tools are not installed, or use different versions from the ones specified in poetry.lock.